Does External Supervision Reduce the Risk Preference on Shadow Banking? (Note 1)——Evidence from Quasi-Natural Experiment Based on “Document No.107” of the State Council and National Audit Notice
Bibliographic record
Abstract
The rapid development of shadow banking and its high-risk problems have got highly concerned from the supervision departments, and they have been supervised from various external aspects. The purpose of this study is to examine whether the administrative supervision can reduce the risk preference of shadow banking effectively from two aspects such as the “Document No.107” of the State Council and national audit. This study quantifies the effect of “Document No. 107” and national audit by the non-observed-effect panel data model and the PSM—DID. The results show that “Document No.107” and national audit can regulate shadow banking significantly by controlling other factors, which is reflected by the fact that the decreasing of shadow banking’s scale and the improvement in risk structure can significantly reduce the risk preference of shadow banking. Since administrative supervision and national audit have different supervisory means and functional mechanisms, the cooperation and complementation between them must be necessary in the future during the regulation of shadow banking. Finally, this paper puts forward corresponding policy recommendations based on financial stability objectives.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".